Color and color patterns have been used to study a wide range of
ecological and evolutionary topics, including sexual selection (Punzalan
et al. 2008), aposematism (Brower 1958), industrial melanism (Kettlewell
1961), and mimicry (Jiggins et al. 2001; Saito 2002). Color is used in
the classification of organisms to verify species and population
properties, and subspecies (Brower 1959). The color of butterfly life
stages and wings is used to understand evolutionary-developmental
patterns and phenotypic plasticity (Starnecker & Hazel 1999; Nice
& Fordyce 2006; Otaki 2008). However, most of these studies are
hindered in their ability to quantify color.

When reporting quantified colors, RGB (red, green, blue) and L*,
a*, and a* values (L* = lightness, scale: 0-100; a* = green to red,
scale: -120-120; and b* v alues = blue to yellow, scale: -120-120) are
typically used. RGB are digitally represented by 256 values each,
meaning a total of more than 16 million possible color combinations
(Balaban 2008), but the colors produced by these values are typically
non-uniform and do not correlate well to human vision (Pedreschi et al.
2006). However, L*, a*, and b* values are combined together to represent
a color that can be used in a comparative context to other similar
colors (Pedreschi et al. 2006), and do account for the way humans
perceive color.

Existing methods for quantifying color include simple visual
estimates, with or without the use of a book of color standards for
reference such as Munsell's (1976), spectrophotometry (Stevens et
al. 2007), color software with RGB applications (Villafuerte & Negro
1998), and colorimetery (Yagiz et al. 2009). Human vision is color
biased (Wyszecki & Stiles 1982); factors such as lighting condition,
illumination, and color are context-dependent (Endler 1990; Zuk &
Decruyenaere 1994), and make color difficult to quantify. Specimens need
to be nearly homogenous in color and have an almost flat surface to be
accurately represented with colorimetry (Balaban 2008; Yagiz et al.
2009), and common image software such as Adobe Photoshop[R] has
limitations when standardizing or calibrating a digital image and when
quantifying the color patterns of complex images with large color
variation.

Our objective was to introduce the use of image analysis with the
LensEye[R] software as a tool to quantify the color of insects.
LensEye[R] software was developed specifically for color quantification
purposes, which makes it more user-friendly than other general color
analysis programs such as Adobe Photoshop[R]. LensEye[R] has been used
in food and agricultural sciences (Balaban 2008; Yagiz et al. 2009), but
its application to entomological studies is novel. To illustrate this
process, the wing colors of male and female Eastern Tiger Swallowtail
butterflies, Papilio glaucus L., were analyzed in 2 comparisons: (1) the
percentage of blue on the hindwing between yellow and dark morph females
of P. glaucus, and (2) the percentage of orange hues between males of
the 2 subspecies P. g. glaucus L. and P. g. maynardi Gauthier. In the
first comparison, we predicted that the percentage of blue on the
hindwing would be similar in yellow and dark morph females, because to
our knowledge no previous reports have suggested a larger amount of blue
in either morph. In the second comparison, we expected that males of P.
g. maynardi would have a significantly larger percentage of the wings
represented by high a* and b* values when compared with P. g. glaucus,
as a combination of these values (reds and yellows) likely produces the
orange hues that are diagnostic for this subspecies. To our knowledge,
this is the first report of color quantification of tiger swallowtail
butterflies.

MATERIALS AND METHODS

Study Species and Specimen Preparation

The Eastern Tiger Swallowtail, Papilio glaucus L. (Lepidoptera:
Papilionidae), is a large multi-colored butterfly found throughout the
eastern half of the USA (Scriber 1996). Females are polymorphic and are
either yellow with black stripes or melanic (Clarke & Sheppard 1962;
Scriber 1996; Scriber et al. 1996); both forms have blue scales along
the submarginal region of the dorsal side of the hindwings. Currently, 2
putative subspecies are recognized, P. g. glaucus and P. g. maynardi;
the latter has a unique orange background color rather than the yellow
found on the glaucus subspecies (Maynard 1891; Scriber 1986). Papilio g.
maynardi is primarily found in Florida, but occasionally is found in
other southeastern states (Maynard 1891; Brower 1959; Scriber 1986;
Lindroth 1991). Ten yellow females and 10 dark morph females of P.
glaucus were captured from Cedar Key and Lake Placid, Florida to compare
the percentage of blue in the hindwings between these morphs. To compare
the percentage of orange hues between the 2 subspecies, 10 males were
collected from La Fayette, Georgia, and 10 males from Lake Placid,
Florida, to represent the P. g. glaucus and P. g. maynardi subspecies,
respectively. All specimens were captured during Apr-Jun, 2008,
representing what is likely the spring brood of P. glaucus in these
regions.

All butterflies were captured with a butterfly net and placed into
glassine envelopes for transport. The live adults of P. glaucus were
cooled in a walk-in refrigerator at 4[degrees]C, removed from the
glassine envelopes, and their wings spread at 4[degrees]C on white
Styrofoam[R] to expose the dorsal side of the wings, positioned as if
prepared for a professional insect collection. Spreading was facilitated
with insect pins placed near the costal and Al veins of the forewing and
the anal vein and distal portion of M3 vein of the hindwing proximal to
the tail. No pins were inserted into the body. Once a butterfly was
spread, it was removed from the walk-in refrigerator and walked to the
equipment for color analysis.

Protocol for Color Analysis

Each butterfly was placed individually in a light-box with D65
standardized lighting (Luzuriaga et al. 1997), and a Labsphere[R] (North
Sutton, NH) yellow color standard was placed next to the butterfly.
Inside the light-box, a Nikon D200 digital camera was fastened to a
stand approximately 0.3 m tall so that the camera faced down, and was
fixed at a specific height and connected to a computer by a USB cable
(camera specifications listed in Table l). The light-box door was closed
and a photograph was taken of the butterfly. Once in the light-box, it
took less than 30 sec to process an individual butterfly. The computer
used Camera ControlPro[R] software (Nikon, Tokyo, Japan) to control the
act of taking a photograph with the camera; therefore, a photograph
could be taken from the computer while the camera was enclosed within
the light-box, and the picture would upload onto the computer. Two types
of software were used for color analysis: Adobe Photoshop 6.0[R] (Adobe
Systems Inc, San Jose, California) used for image adjustments,
modifications, and edits, and LensEye[R] (Engineering and
CyberSolutions, Gainesville, Florida), used for color quantification and
analysis).

The digital photographs (JPEG) (Fig. la) were cleaned in Adobe
Photoshop 6.0[R] to isolate the images necessary for color analysis. The
"eraser" tool was used to remove insect pins, feces, and
additional artifacts created during photographing. The image of the
Labsphere[R] color standard was cleaned by selecting the
"elliptical marquee" tool that was used to highlight a yellow
circular area within the color standard, which was moved with the
"move" tool to the left of the butterfly, and the remainder of
the color standard was erased. This process created 2 final images: a
butterfly and a yellow circle. The image resolution was adjusted to 700
pixels wide by selecting "Image" in the main toolbar, then
"resize" and "image size", and saved as a 24 bit BMP
image (Fig. lb). Females of P. glaucus were cleaned with the use of the
"eraser" tool until only one hindwing remained. Males of P. g.
glaucus and P. g. maynardi were cleaned so the entire butterfly (minus
antennae) remained.

Cleaned images were opened and analyzed in LensEye[R] software. In
Lenseye[R], the objects of interest were separated from the background
by designating the background color to consist of any pixel with RGB
colors between 220 and 255, and the "16 colors per axis (4096 color
blocks)" option was selected. This color information was displayed
as the "% of total object area." Objects smaller than a
user-selected threshold of 100 pixels were ignored, ensuring only the
butterfly and color standard would be analyzed. In the color calibration
option, the L*, a*, and b* values of the color standard were entered
(L*, a*, and b* value of 90.17, -3.27, and 74.30, respectively), and the
image was calibrated by selecting the "Process Image" tab. The
software then calculated the average L*, a*, and b* values of the color
standard from the uncalibrated image, and adjusted the color of each
pixel in the image so that the average color of the standard in the
image would equal that of the given reference values; this process
calibrated all objects in the image (Fig. 1c). A spreadsheet was
produced listing the percentage of each color (color ID#) and the
average and standard deviation of the L*, a*, and b* values based on
each pixel in the object. Each color ID # has a unique L*, a*, and b*
value (Table 2), and the in formation for each color was provided in the
"color block information" in the software.

[FIGURE 1 OMITTED]

Both comparisons required the use of the "color contours"
option in LensEye[R] software. For the first comparison, the most
abundant colors of blue (color ID #) were selected from the spreadsheet
and the L*, a*, and b* values of these colors were searched for in the
"color block information" option. To analyze the calibrated
image, it had to be reopened and reprocessed in LensEye[R]. The
"show contours" option was selected revealing a table with
options for selecting thresholds, where the blue L*, a*, and b* values
were entered. On the image, the L*, a*, and b* contour settings were
manipulated by interactively adjusting them and evaluating the quantity
of blue pixels that were highlighted in the image to find the range of
blue color values that encompassed the entire blue area on the
butterfly. After 2 images of both yellow and dark morph females were
manipulated, the following settings were deemed best suited for the
task: L* contour greater than 20, a* contour less than 19, b* contour
less than 25. These threshold values were entered for each of the 20
images and the software selected all the pixels that met the above
criteria (all blue areas were highlighted in red, Fig. 2A). The
percentages of blue colors of the total wing area were recorded for each
image by selecting the "report contour" option.

For the second comparison, we used a male of P. g. maynardi from
Lake Placid, Florida, to determine color composition to represent the
maynardi subspecies. The image of this male was calibrated to receive
the spreadsheet with the color ID # information, and the color
moderate-orange-yellow (L*, a*, and b* values equal to 70, 9, and 60,
respectively) was chosen to represent the threshold to distinguish P. g.
maynardi from P. g. glaucus. This color was chosen because it was the
lightest orange hue represented by the specimen in the image, and we
also wanted to include darker hues of orange in our analysis, as these
colors also may be present on the wings of P. g. maynardi. Calibrated
images of the males were reopened in LensEye[R] and reprocessed. The
"show contours" option was selected and the L*, a*, and b*
contour values were entered into the threshold space. All values greater
than the chosen threshold values were highlighted, because these values
(higher a* and b* values) would represent darker orange colors in the
butterfly wings than the moderate-orange-yellow color (Fig. 2B). The
"report contour" option was chosen to record the percentage of
wing area highlighted.

Statistical Analysis

We used a Welch's t test (two-tailed; P = 0.05) to evaluate
differences in the percentage of blue between yellow and dark morph
females, and the percentage of orange on the wings of males of the 2
subspecies.

[FIGURE 2 OMITTED]

RESULTS

The dark morph and yellow females did not differ significantly in
the percentage of blue on the hindwing (mean [+ or -] SE) (16.98 percent
[+ or -] 3.10 and 14.2 percent [+ or -] 1.4, respectively) (t = 1.5858;
df = 18; P = 0.1411). However, the pattern of blue differed between the
morphs (Fig. 2A). All yellow females had blue scales restricted to the
submarginal area of the hindwing, resulting in less than 20% of blue
color on the hindwing, which was similar to some dark females, but other
dark females had blue that continued proximally and became more random
and scattered, resulting in a larger variation of blue color in these
morphs. Four of the dark morph females had over 20% of blue scales on
the hindwing, synonymous with the scattered blue scale phenotype, but
the large variation in this morph led to an average quantity of blue not
significantly different from that of the yellow morph.

Males of Papilio glaucus maynardi from Lake Placid, Florida, had
significantly more orange than the butterflies from La Fayette, Georgia
(9.97 percent [+ or -] 2.18 and 0.52 percent [+ or -] 0.90,
respectively) (t = 4.007; df = 18; P = 0.0021), 80% of the analyzed P.
glaucus from La Fayette had 0% of the wings at or above the designated
L*, a*, and b* threshold used to represent moderate-orange-yellow.
Although P. g. maynardi from Lake Placid, Florida, was visually distinct
from the northern subspecies, the range of orange hues on the wings
would have been difficult to quantify without a computer vision system
and image analysis software. Lenseye[R] highlighted only the areas of
the wings we were interested in analyzing. Even small patches of blue in
the hindwing were highlighted, verifying the software's sensitivity
to interpreting specified colors in an intricate color pattern.

DISCUSSION

The application of image analysis software and our methods open a
new avenue for quantifying color that could influence understanding of
color components in ecological and evolutionary systems. For instance,
color associated with the effects of temperature or host plant
(phenotypic plasticity) (Price 2006), range distributions of hybrid
zones (Blum 2002; Gay et al. 2008), floral color changes in response to
insect pollination (Paige & Whitham 1985), and seasonal polyphenisms
(Hazel 2002) can be quantified. This study also provides a means to
analyze color of live specimens, which could have important implications
to studies of endangered species. In this study, the butterflies seemed
unaffected by the method, and were capable of flight, copulation, and
oviposition after the study, verified by additional studies (M.S.L.,
unpublished data). Our methods also provide a protocol to quantify
museum specimens, for instance, in studying how color dynamics of
populations have shifted over time.

Our method allows the use of thresholds to study colors of interest
and to determine their percentage compared with the rest of the image.
For example, the blue scales scattered over the hind-wing of a dark
morph female were quantified, even though these small blue spots were on
a black background. Additionally, similar, but different, colors
(yellow-orange) were quantified to distinguish 2 entities. Papilio
glaucus maynardi is relatively unstudied, and there are conflicting
reports concerning its distribution (Forbes I960; Harris 1972; Howe
1975; Mather & Mather 1985; Scriber 1986; Lindroth et al. 1988). Our
method could provide a means to determine its distribution. Other
aspects of its evolutionary history could be addressed, such as
determining if the subspecies represent a color cline or a rapid shift
in color, suggesting similar dynamics of a narrow hybrid zone where one
phenotype rapidly shifts to the other.

The primary limitation of our method, and other color
quantification methods, is that standardized lighting is necessary;
therefore, these methods would not be reliable in all situations, such
as comparing the color of butterfly wings from photographs taken
outdoors under different lighting conditions. We addressed this issue by
using a light-box with standardized lighting. Other source and
processing errors may have occurred, such as instrumental inaccuracies
of the light-box, camera, and software; however, to minimize these
errors we used the same camera and light specifications for each
individual. In addition, there may be a source error in that populations
of P. glaucus may experience a seasonal polyphenism, which could alter
our interpretations of the data sets. We addressed this issue by
collecting the individuals from the various locations during a similar
time period.

ACKNOWLEDGMENTS

We thank Jonathan Doyle and Matthew Standridge (both at McGuire
Center for Lepidoptera and Biodiversity, University of Florida,
Gainesville) for technical assistance in cleaning up photographs for
analysis and for testing the protocol, and Alberto De Azeredo (Food
Science and Human Nutrition Department, University of Florida,
Gainesville) for camera, software, and photograph assistance. Jonathan
Doyle assisted in collecting the P. glaucus. We thank Peter Adler
(Department of Entomology, Soils, and Plant Sciences, Clemson
University, Clemson), and Richard Lehnert, and 3 anonymous reviewers for
editorial comments on the manuscript.